Update app.py

app.py

@@ -1,49 +1,48 @@
 import gradio as gr
 import joblib
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# ✅ Load local model and tokenizer from uploaded folder
-model_dir = "./campaign-bert-model"
+# ✅ Use folder name directly (no ./)
+model_dir = "campaign-bert-model"
 
 try:
     tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False)
     model = AutoModelForSequenceClassification.from_pretrained(model_dir)
     label_encoder = joblib.load("label_encoder.pkl")
-    print("✅ Model, Tokenizer, and Label Encoder loaded successfully!")
+    print("✅ Model, Tokenizer, and Label Encoder loaded.")
 except Exception as e:
     raise RuntimeError(f"❌ Failed to load model or tokenizer: {e}")
 
-# ✅ Prediction function
+# ✅ Prediction logic
 def predict(client_interest, sentiment):
     try:
         text = f"{client_interest} Sentiment: {sentiment}"
-        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
         outputs = model(**inputs)
-        predicted_label_id = torch.argmax(outputs.logits, dim=1).item()
-        predicted_label = label_encoder.inverse_transform([predicted_label_id])[0]
-        confidence = torch.softmax(outputs.logits, dim=1)[0][predicted_label_id].item()
-
-        return f"🎯 Predicted Template: **{predicted_label}**\n✅ Confidence: **{confidence:.2%}**"
+        predicted_id = torch.argmax(outputs.logits, dim=1).item()
+        label = label_encoder.inverse_transform([predicted_id])[0]
+        confidence = torch.softmax(outputs.logits, dim=1)[0][predicted_id].item()
+        return f"🎯 Template: **{label}**\n📊 Confidence: **{confidence:.2%}**"
     except Exception as err:
-        return f"❌ Error during prediction: {str(err)}"
+        return f"❌ Prediction failed: {err}"
 
-# ✅ Gradio UI
+# ✅ UI with styling
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🎨 Campaign Personalizer")
-    gr.Markdown("Predict the best template ID based on interest and sentiment.")
+    gr.Markdown("## 🎯 Campaign Personalizer")
+    gr.Markdown("Provide interest and sentiment to get the best marketing template.")
 
     with gr.Row():
         with gr.Column():
-            interest = gr.Textbox(label="📝 Client Interest", placeholder="e.g., Interested in Term Plan")
-            sentiment = gr.Textbox(label="😊 Sentiment", placeholder="e.g., positive / neutral / negative")
-            submit_btn = gr.Button("🔍 Predict Template")
-
+            interest = gr.Textbox(label="📝 Client Interest")
+            sentiment = gr.Textbox(label="😊 Sentiment")
+            submit = gr.Button("🔍 Predict")
         with gr.Column():
-            output = gr.Markdown(label="🎯 Output")
+            result = gr.Markdown(label="🎯 Output")
 
-    submit_btn.click(fn=predict, inputs=[interest, sentiment], outputs=output)
+    submit.click(predict, inputs=[interest, sentiment], outputs=result)
 
-# ✅ Launch
+# ✅ Launch app
 if __name__ == "__main__":
     demo.launch()
+